原子氧对紫外光固化有机硅环氧树脂的作用

通过对光固化有机硅环氧树脂进行地面模拟原子氧效应试验,利用扫描电镜（SEM）和X射线 光电子能谱（XPS）等分析了试验前后试样的质量损失、表面形貌和表面成分等的变化。研 究结果表明,有机硅环氧树脂经紫外光固化后,试样表面被部分氧化,硅氧主链结构中的－ R（甲基或苯基）侧基被不完全氧化成含O的基团结构。在7.43×10 15 atoms／
cm 2·s通量的原子氧暴露8h试验后,试样表面形成一层氧化硅（SiOx）膜,膜 中含65％的SiO2,有助于防止原子氧对材料的进一步侵蚀。     

体积激励法测量液体推进剂量的地面模拟试验

针对大贮箱推进剂量测量精度不高问题,研究一种测量精度较高、重复性强的液体推进剂量测量方法。首先介绍体积激励法测量推进剂量的测量原理,重点分析地面模拟试验装置组成,包括体积激励装置、编码电机控制、数据采集及数据处理分系统。其次阐述试验方案,试验在常温常压下进行,采用不同激励频率改变体积。最后,以水作为模拟推进剂开展地面模拟试验,结果表明在不同填充水平下液体推进剂量测量误差都控制在贮箱总体积的1%以内,为未来高精度测量推进剂量飞行试验及空间应用提供可靠支持。     

考虑观测的双冲量最优交会研究

针对跟踪星主动交会目标星的轨道转移问题,给出了考虑观测的双冲量最优交会迭代算法。首先对考虑观测的轨道交会问题进行了分析,给出了椭圆转移轨道的存在性判定方法。在此基础上,给出了基于粒子群的优化求解方案。经过对两异面椭圆轨道转移问题的仿真解算,求得了最优交会轨道,验证了算法的正确性。该研究成果可作为工程应用的有益参考,并为其他带约束的轨道交会优化问题提供可行的求解方案。     

弹体结构平方米级扇形区域脉冲外压加载技术

针对弹体外表面平方米级区域的高峰值、短脉宽压力加载难题,依据一维可压缩流设计了高压腔、膜片腔、试验腔的气腔加载系统,附加了与试验件随形、扩展加压面积、均化压力分布的水腔,实现了平方米级区域1 MPa峰值、10 ms上升沿的脉冲压力加载要求,建立了弹性水腔的两自由度压力分析模型,获得了水腔系统的动特性参数,计算拟合曲线与...     

自动相关监视系统信息传输性能分析方法及测试平台构建

本研究目的在于提出一种在民用飞机机载自动相关监视系统(Automatic Dependent Surveillance,简称ADS)系统设计和试验过程中实时/非实时检测该系统端到端信息传输性能的分析方法,并进行相应的地面测试平台搭建设计。对口于飞机的机载系统,ADS监视系统信息传输性能分析平台能够快速准确定位ADS系统的试验故障,实现有效的排故和分析,直观地评估自相关监视系统的工作状态。这项研究的结果有助于推动机载合同式自动相关监视系统(Automatic Dependent Surveillance-Contract,简称ADS-C)和广播式自动相关监视系统(Automatic Dependent Surveillance-Broadcast,简称ADS-B)机上地面功能试验、民机机上地面试验、民机飞行试验等各项工作的开展。该平台在飞机交付以后,也有利于维护工作与客户支持工作的进行。     

基于压力信号器控制的贮箱地面增压技术应用分析

对基于压力信号器控制的贮箱地面增压技术及其在发射场的实际应用情况进行了介绍分析。常温贮箱射前地面增压时间较为宽裕，一般采用单气路单压力信号器控制增压；低温贮箱射前地面增压时间较短，要求较快的增压速率，一般采用双气路双压力信号器控制增压。贮箱射前地面增压存在压力后效，影响压力后效大小的主要因素包括：增压充气流量qm、增压电磁阀动作响应时间Δt、增压电磁阀后供气管路气体容积Vg。发射场实际应用过程中，通过调整供气管路上节流孔板大小控制增压充气流量，使贮箱增压压力后效满足要求。针对压力信号器失效故障模式，通过设置紧急放气电磁阀和冗余设计,实现紧急放气和冗余备保增压，可以确保贮箱射前增压工作正常。     

Real-time modular dynamic modeling for compression system of altitude ground test facilities

Modeling of a centrifugal compressor is of great significance to surge characteristics and fluid dynamics in the Altitude Ground Test Facilities (AGTF). Real-time Modular Dynamic System Greitzer (MDSG) modeling for dynamic response and simulation of the compression system is introduced. The centrifugal compressor, pipeline network, and valve are divided into pressure output type and mass flow output type for module modeling, and the two types of components alternate when the system is established. The pressure loss and thermodynamics of the system are considered. An air supply compression system of AGTF is modeled and simulated by the MDSG model. The simulation results of mass flow, pressure, and temperature are compared with the experimental results, and the error is less than 5%, which demonstrates the reliability, practicability, and universality of the MDSG model.     

电帆推进在等离子体环境下推力影响因素分析和试验研究

为了分析电帆推力的影响因素，根据电帆推进原理设计了电帆地面模拟装置。首先分析了真空中带电导线和等离子作用机理，得出了电帆推力公式。利用朗缪尔探针测量了电帆模拟试验环境中等离子体参数，计算产生推力的大小，最后通过微推力测量系统测量电帆产生的推力，与计算结果进行了对比。根据推力公式和仿真结果表明，提高电压、增加带电粒子密度和速度、增加电帆长度均可增加推力。在试验中电帆上电压从300V增加到800V时，推力从9μN增加到26μN。仿真和试验结果表明，电压、带电粒子密度和速度、电帆导线长度都对推力有影响。根据试验结果，电帆推力公式的经验推力系数在1～2取值。     

Developing a novel radial ultrasonic vibration-assisted grinding device and evaluating its performance in machining PTMCs

Particle-reinforcing titanium matrix composites(PTMCs) exhibit the sharp raising applications in modern industries owing to its extraordinary physical and mechanical properties. However, the poor grindability and unstable grinding processes due to the existence of TiC particles and TiB short fibres inside PTMCs, leading to the sudden grinding burn and low material removal rate.In this work, a novel radial ultrasonic vibration-assisted grinding(RUVAG) device with a special cross structure was dev...     

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning (RL) techniques are being studied to solve the Demand and Capacity Balancing (DCB) problems to fully exploit their computational performance. A locally generalised Multi-Agent Reinforcement Learning (MARL) for real-world DCB problems is proposed. The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management (ATFM) region to quickly obtain a satisfactory solution. In this method, agents of all flights in a scenario form a multi-agent decision-making system based on partial observation. The trained agent with the customised neural network can be deployed directly on the corresponding flight, allowing it to solve the DCB problem jointly. A cooperation coefficient is introduced in the reward function, which is used to adjust the agent’s cooperation preference in a multi-agent system, thereby controlling the distribution of flight delay time allocation. A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated. Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method. From a statistical point of view, it is proven that the proposed method is generalised within the scope of the flights and sectors of interest, and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods. The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.